DMET™ (Drug Metabolism Enzymes and Transporters): a pharmacogenomic platform for precision medicine

Pharmacogenomics revolutionizing cardiovascular therapeutics: A narrative review

Background and Aim

Among the cardiovascular diseases (CVDs), heart failure, hypertension, and myocardial infarction are associated with the greatest number of disability-adjusted life years due to lifestyle changes and the failure of therapeutic approaches, especially the one-size-fits-all interventions. As a result, there has been advances in defining genetic variants responsible for different responses to cardiovascular drugs such as antiplatelets, anticoagulants, statins, and beta-blockers, which has led to their usage in guiding treatment plans. This study comprehensively reviews the current state-of-the-art potential of pharmacogenomics in dramatically altering CVD treatment. It stresses the applicability of pharmacogenomic technology, the threats associated with its adoption in the clinical setting, and proffers relevant solutions.

Methods

Literature search strategies were used to retrieve articles from various databases: PubMed, Google Scholar, and EBSCOhost. Articles with information relevant to pharmacogenomics, DNA variants, cardiovascular diseases, sequencing techniques, and drug responses were reviewed and analyzed.

Results

DNA-based technologies such as next generation sequencing, whole genome sequencing, whole exome sequencing, and targeted segment sequencing can identify variants in the human genome. This has played a substantial role in identifying different genetic variants governing the poor response and adverse effects associated with cardiovascular drugs. Thus, this has reduced patients' number of emergency visits and hospitalization.

Conclusion

Despite the emergence of pharmacogenomics, its implementation has been threatened by factors including patient compliance and a low adoption rate by clinicians. Education and training programs targeting both healthcare professionals and patients should be established to increase the acceptance and application of the emerging pharmacogenomic technologies in reducing the burden of CVDs.

Pharmacogene expression during progression of metabolic dysfunction-associated steatotic liver disease: Studies on mRNA and protein levels and their relevance to drug treatment

Metabolic dysfunction-associated steatotic liver disease (MASLD) is common worldwide. Genes and proteins contributing to drug disposition may show altered expression as MASLD progresses. To assess this further, we undertook transcriptomic and proteomic analysis of 137 pharmacogenes in liver biopsies from a large MASLD cohort. We performed sequencing on RNA from 216 liver biopsies (206 MASLD and 10 controls). Untargeted mass spectrometry proteomics was performed on a 103 biopsy subgroup. Selected RNA sequencing signals were replicated with an additional 187 biopsies. Comparison of advanced MASLD (fibrosis score 3/4) with milder disease (fibrosis score 0-2) by RNA sequencing showed significant alterations in expression of certain phase I, phase II and ABC transporters. For cytochromes P450, CYP2C19 showed the most significant decreased expression (30 % of that in mild disease) but significant decreased expression of other CYPs (including CYP2C8 and CYP2E1) also occurred. CYP2C19 also showed a significant decrease comparing the inflammatory form of MASLD (MASH) with non-MASH biopsies. Findings for CYP2C19 were confirmed in the replication cohort. Proteomics on the original discovery cohort confirmed decreased levels of several CYPs as MASLD advanced but this decrease was greatest for CYP2C19 where levels fell to 40 % control. This decrease may result in decreased CYP2C19 activity that could be problematic for prescription of drugs activated or metabolized by CYP2C19 as MASLD advances. More limited decreases for other P450s suggest fewer issues with non-CYP2C19 drug substrates. Negative correlations at RNA level between CYP2C19 and several cytokine genes provided initial insights into the mechanism underlying decreased expression.

Identification of ADME genes polymorphic variants linked to trastuzumab-induced cardiotoxicity in breast cancer patients: Case series of mono-institutional experience

BACKGROUND

Long-term survival induced by anticancer treatments discloses emerging frailty among breast cancer (BC) survivors. Trastuzumab-induced cardiotoxicity (TIC) is reported in at least 5% of HER2+BC patients. However, TIC mechanism remains unclear and predictive genetic biomarkers are still lacking. Interaction between systemic inflammation, cytokine release and ADME genes in cancer patients might contribute to explain mechanisms underlying individual susceptibility to TIC and drug response variability. We present a single institution case series to investigate the potential role of genetic variants in ADME genes in HER2+BC patients TIC experienced.

METHODS

We selected data related to 40 HER2+ BC patients undergone to DMET genotyping of ADME constitutive variant profiling, with the aim to prospectively explore their potential role in developing TIC. Only 3 patients ("case series"), who experienced TIC, were compared to 37 "control group" matched patients cardiotoxicity-sparing. All patients underwent to left ventricular ejection fraction (LVEF) evaluation at diagnosis and during anti-HER2 therapy. Each single probe was clustered to detect SNPs related to cardiotoxicity.

RESULTS

In this retrospective analysis, our 3 cases were homogeneous in terms of clinical-pathological characteristics, trastuzumab-based treatment and LVEF decline. We identified 9 polymorphic variants in 8 ADME genes (UGT1A1, UGT1A6, UGT1A7, UGT2B15, SLC22A1, CYP3A5, ABCC4, CYP2D6) potentially associated with TIC.

CONCLUSION

Real-world TIC incidence is higher compared to randomized clinical trials and biomarkers with potential predictive value aren't available. Our preliminary data, as proof of concept, could suggest a predictive role of pharmacogenomic approach in the identification of cardiotoxicity risk biomarkers for anti-HER2 treatment.

Genetic Biomarkers of Sorafenib Response in Patients with Hepatocellular Carcinoma

The identification of biomarkers for predicting inter-individual sorafenib response variability could allow hepatocellular carcinoma (HCC) patient stratification. SNPs in angiogenesis- and drug absorption, distribution, metabolism, and excretion (ADME)-related genes were evaluated to identify new potential predictive biomarkers of sorafenib response in HCC patients. Five known SNPs in angiogenesis-related genes, including VEGF-A, VEGF-C, HIF-1a, ANGPT2, and NOS3, were investigated in 34 HCC patients (9 sorafenib responders and 25 non-responders). A subgroup of 23 patients was genotyped for SNPs in ADME genes. A machine learning classifier method was used to discover classification rules for our dataset. We found that only the VEGF-A (rs2010963) C allele and CC genotype were significantly associated with sorafenib response. ADME-related gene analysis identified 10 polymorphic variants in ADH1A (rs6811453), ADH6 (rs10008281), SULT1A2/CCDC101 (rs11401), CYP26A1 (rs7905939), DPYD (rs2297595 and rs1801265), FMO2 (rs2020863), and SLC22A14 (rs149738, rs171248, and rs183574) significantly associated with sorafenib response. We have identified a genetic signature of predictive response that could permit non-responder/responder patient stratification. Angiogenesis- and ADME-related genes correlation was confirmed by cumulative genetic risk score and network and pathway enrichment analysis. Our findings provide a proof of concept that needs further validation in follow-up studies for HCC patient stratification for sorafenib prescription.

Pharmacogenetic Analysis of an 8-Year Old Girl with Reye Syndrome Associated with Use of Naproxen

BACKGROUND Reye syndrome is a rare, yet potentially life-threatening disease characterized by acute encephalopathy and hepatic failure. This report presents the case of an 8-year-old girl with Reye syndrome and seizures after the use of naproxen. CASE REPORT An 8-year-old girl experienced a 3-day episode of fever and abdominal pain. After receiving naproxen (375 mg twice daily) starting from day -3, she exhibited hypotension, tonic seizure, and loss of consciousness (day 1). Physical examination and laboratory test results revealed acute kidney injury, metabolic acidosis, and elevated levels of lactate dehydrogenase (LDH), liver enzymes, and ferritin. On day 2, the maximum values of aspartate aminotransferase, alanine aminotransferase, LDH, creatinine, and ferritin were 955 U/L, 132 U/L, 8040 U/L, 2 mg/dL, and >40000 ug/L, respectively. She was given supportive care and recovered after 11 days (day 12), with normalization of kidney function and metabolic abnormalities. To identify possible genetic polymorphisms associated with the patient's symptoms, genotypes were tested using a drug metabolizing enzymes and transporters (DMET) gene chip. Among genes involved in the metabolism of naproxen, UGT1A6 (*1/*2) and UGT2B7 (*1/*2) resulted in possibly decreased function. Other results which may have had clinical significance included homozygote results for NAT2*6/*6 (rs1799930). CONCLUSIONS A rare case of Reye syndrome after administration of naproxen was presented in this case. A DMET gene chip was used to screen for possible genetic polymorphisms associated with Reye syndrome, but the result was inconclusive.

Orally Administered Drugs and Their Complicated Relationship with Our Gastrointestinal Tract

Orally administered compounds represent the great majority of all pharmaceutical compounds produced for human use and are the most popular among patients since they are practical and easy to self-administer. Following ingestion, orally administered drugs begin a "perilous" journey down the gastrointestinal tract and their bioavailability is modulated by numerous factors. The gastrointestinal (GI) tract anatomy can modulate drug bioavailability and accounts for interpatient drug response heterogeneity. Furthermore, host genetics is a contributor to drug bioavailability modulation. Importantly, a component of the GI tract that has been gaining notoriety with regard to drug treatment interactions is the gut microbiota, which shares a two-way interaction with pharmaceutical compounds in that they can be influenced by and are able to influence administered drugs. Overall, orally administered drugs are a patient-friendly treatment option. However, during their journey down the GI tract, there are numerous host factors that can modulate drug bioavailability in a patient-specific manner.

Pharmacogenetics of ABCB1, CDA, DCK, GSTT1, GSTM1 and outcomes in a cohort of pediatric acute myeloid leukemia patients from Colombia

BACKGROUND AND AIM

Different studies have shown pharmacogenetic variants related to drug toxicity in acute myeloid leukemia (AML) patients. Our aim was to identify the association between ABCB1, CDA, DCK, GSTT1, and GSTM1 variants with clinical outcomes and toxicity in pediatric patients with AML.

METHODS

Fifty-one confirmed de novo AML pediatric patients were included. A SNaPshot™ assay and conventional PCR were used to evaluate ABCB1, CDA, DCK, GSTT1, and GSTM1 variants. Clinical outcomes and toxicity associations were evaluated using odds ratios and Chi-square analysis.

RESULTS

Patients carrying ABCB1 (1236C > T, rs1128503) GG genotype in had a 6.8 OR (CI 95% 1.08-42.73, p = .044) for cardiotoxicity as compared to patients carrying either AA or GA genotypes 0.14 OR (CI 95% 0.023-0.92, p = .044). For ABCB1 (1236G > A rs1128503/2677C > A/T rs2032582/3435G > A rs1045642) AA/AA/AA combined genotypes had a strong association with death after HSTC OR 13.73 (CI 95% 1.94-97.17, p = .009). Combined genotypes GG/CC/GG with CDA (79A > C, rs2072671) CA genotype or CDA (-451G > A, rs532545) CT genotype, had a 4.11 OR (CI 95% 2.32-725, p = .007) and 3.8 OR (CI 95% 2.23-6.47, p = .027) with MRD >0.1% after first chemotherapy cycle, respectively.

CONCLUSION

Our results highlight the importance of pharmacogenetic analysis in pediatric AML, particularly in populations with a high degree of admixture, and might be useful as a future tool for patient stratification for treatment.

Association between Germline Single-Nucleotide Variants in ADME Genes and Major Molecular Response to Imatinib in Chronic Myeloid Leukemia Patients

Imatinib is the most common first-line tyrosine kinase inhibitor (TKI) used to treat chronic-phase chronic myeloid leukemia (CP-CML). However, only a proportion of patients achieve major molecular response (MMR), so there is a need to find biological factors that aid the selection of the optimal therapeutic strategy (imatinib vs. more potent second-generation TKIs). The aim of this retrospective study was to understand the contribution of germline single-nucleotide variants (gSNVs) in the achievement of MMR with imatinib. In particular, a discovery cohort including 45 CP-CML patients was analyzed through the DMET array, which interrogates 1936 variants in 231 genes related to the absorption, distribution, metabolism and excretion (ADME) process. Variants statistically significant in the discovery cohort were then tested in an extended and independent cohort of 137 CP-CML patients. Finally, a total of 7 gSNVs (ABCG1-rs492338, ABCB11-rs496550, ABCB11-rs497692, CYP2D6-rs1135840, CYP11B1-rs7003319, MAT1A-rs4934027 and SLC22A1-rs628031) and one haplotype in the ABCB11 gene were significantly associated with the achievement of MMR with first-line imatinibtreatment. In conclusion, we identified a genetic signature of response to imatinib in CP-CML patients that could be useful in selecting those patients that may benefit from starting imatinib as first-line therapy, therefore avoiding the toxicity related to second-generation TKIs.

A Python Clustering Analysis Protocol of Genes Expression Data Sets

Gene expression and SNPs data hold great potential for a new understanding of disease prognosis, drug sensitivity, and toxicity evaluations. Cluster analysis is used to analyze data that do not contain any specific subgroups. The goal is to use the data itself to recognize meaningful and informative subgroups. In addition, cluster investigation helps data reduction purposes, exposes hidden patterns, and generates hypotheses regarding the relationship between genes and phenotypes. Cluster analysis could also be used to identify bio-markers and yield computational predictive models. The methods used to analyze microarrays data can profoundly influence the interpretation of the results. Therefore, a basic understanding of these computational tools is necessary for optimal experimental design and meaningful data analysis. This manuscript provides an analysis protocol to effectively analyze gene expression data sets through the K-means and DBSCAN algorithms. The general protocol enables analyzing omics data to identify subsets of features with low redundancy and high robustness, speeding up the identification of new bio-markers through pathway enrichment analysis. In addition, to demonstrate the effectiveness of our clustering analysis protocol, we analyze a real data set from the GEO database. Finally, the manuscript provides some best practice and tips to overcome some issues in the analysis of omics data sets through unsupervised learning.

Cas9 targeted nanopore sequencing with enhanced variant calling improves CYP2D6-CYP2D7 hybrid allele genotyping

CYP2D6 is a very important pharmacogene as it is responsible for the metabolization or bioactivation of 20 to 30% of the clinically used drugs. However, despite its relatively small length of only 4.4 kb, it is one of the most challenging pharmacogenes to genotype due to the high similarity with its neighboring pseudogenes and the frequent occurrence of CYP2D6-CYP2D7 hybrids. Unfortunately, most current genotyping methods are therefore not able to correctly determine the complete CYP2D6-CYP2D7 sequence. Therefore, we developed a genotyping assay to generate complete allele-specific consensus sequences of complex regions by optimizing the PCR-free nanopore Cas9-targeted sequencing (nCATS) method combined with adaptive sequencing, and developing a new comprehensive long read genotyping (CoLoRGen) pipeline. The CoLoRGen pipeline first generates consensus sequences of both alleles and subsequently determines both large structural and small variants to ultimately assign the correct star-alleles. In reference samples, our genotyping assay confirms the presence of CYP2D6-CYP2D7 large structural variants, single nucleotide variants (SNVs), and small insertions and deletions (INDELs) that go undetected by most current assays. Moreover, our results provide direct evidence that the CYP2D6 genotype of the NA12878 DNA should be updated to include the CYP2D6-CYP2D7 *68 hybrid and several additional single nucleotide variants compared to existing references. Ultimately, the nCATS-CoLoRGen genotyping assay additionally allows for more accurate gene function predictions by enabling the possibility to detect and phase de novo mutations in addition to known large structural and small variants.

During the last decades, the usefulness of personalized medicine has become increasingly apparent. Directly linked to that is the need for accurate genotyping assays to determine the pharmacogenetic profile of patients. Continuing research has led to the development of genotyping assays that perform quite robustly. However, complex genes remain an issue when it comes to determining the complete sequence correctly. An example of such a complex but very important pharmacogene is CYP2D6. Therefore, we developed a genotyping assay in an attempt to generate complete allele-specific consensus sequences of CYP2D6, by optimizing a targeted amplification-free long-read sequencing method and developing a new analysis pipeline. In reference samples, we showed that our genotyping assay performed accurately and confirmed the presence of variants that go undetected by most current assays. However, the implementation of this assay in practice is still hampered as the selected enrichment strategies inherently lead to a low percentage of on-target reads, resulting in low on-target sequencing depths. Further optimization and validation of the assay is thus needed, but definitely worth considering for follow-up research as we already demonstrated the added value for generating more complete genotypes, which on its turn will result in more accurate gene function predictions.

Association between Breast Cancer Polygenic Risk Score and Chemotherapy-Induced Febrile Neutropenia: Null Results

BACKGROUND

The hypothesis that breast cancer (BC) susceptibility variants are linked to chemotherapy-induced toxicity has been previously explored. Here, we investigated the association between a validated 313-marker-based BC polygenic risk score (PRS) and chemotherapy-induced neutropenia without fever and febrile neutropenia (FNc) in Asian BC patients.

METHODS

This observational case-control study of Asian BC patients treated with chemotherapy included 161 FNc patients, 219 neutropenia patients, and 936 patients who did not develop neutropenia. A continuous PRS was calculated by summing weighted risk alleles associated with overall, estrogen receptor- (ER-) positive, and ER-negative BC risk. PRS distributions neutropenia or FNc cases were compared to controls who did not develop neutropenia using two-sample t-tests. Odds ratios (OR) and corresponding 95% confidence intervals were estimated for the associations between PRS (quartiles and per standard deviation (SD) increase) and neutropenia-related outcomes compared to controls.

RESULTS

PRS distributions were not significantly different in any of the comparisons. Higher PRS_overall quartiles were negatively correlated with neutropenia or FNc. However, the associations were not statistically significant (PRS per SD increase OR neutropenia: 0.91 [0.79-1.06]; FNc: 0.87 [0.73-1.03]). No dose-dependent trend was observed for the ER-positive weighted PRS (PRS_ER-pos) and ER-negative weighted PRS (PRS_ER-neg).

CONCLUSION

BC PRS was not strongly associated with chemotherapy-induced neutropenia or FNc.

A Prognostic and Carboplatin Response Predictive Model in Ovarian Cancer: A Mono-Institutional Retrospective Study Based on Clinics and Pharmacogenomics

Carboplatin is the cornerstone of ovarian cancer (OC) treatment, while platinum-response, dependent on interindividual variability, is the major prognostic factor for long-term outcomes. This retrospective study was focused on explorative search of genetic polymorphisms in the Absorption, Distribution, Metabolism, Excretion (ADME) genes for the identification of biomarkers prognostic/predictive of platinum-response in OC patients. Ninety-two advanced OC patients treated with carboplatin-based therapy were enrolled at our institution. Of these, we showed that 72% of patients were platinum-sensitive, with a significant benefit in terms of OS (p = 0.001). We identified an inflammatory-score with a longer OS in patients with lower scores as compared to patients with the maximum score (p = 0.001). Thirty-two patients were genotyped for 1931 single nucleotide polymorphisms (SNPs) and five copy number variations (CNVs) by the DMET Plus array platform. Among prognostic polymorphisms, we found a potential role of UGT2A1 both as a predictor of platinum-response (p = 0.01) and as prognostic of survival (p = 0.05). Finally, we identified 24 SNPs related to OS. UGT2A1 correlates to an "inflammatory-score" and retains a potential prognostic role in advanced OC. These data provide a proof of concept that warrants further validation in follow-up studies for the definition of novel biomarkers in this aggressive disease.

Clinical pharmacogenomic testing and reporting: A technical standard of the American College of Medical Genetics and Genomics (ACMG)

Pharmacogenomic testing interrogates germline sequence variants implicated in interindividual drug response variability to infer a drug response phenotype and to guide medication management for certain drugs. Specifically, discrete aspects of pharmacokinetics, such as drug metabolism, and pharmacodynamics, as well as drug sensitivity, can be predicted by genes that code for proteins involved in these pathways. Pharmacogenomics is unique and differs from inherited disease genetics because the drug response phenotype can be drug-dependent and is often unrecognized until an unexpected drug reaction occurs or a patient fails to respond to a medication. Genes and variants with sufficiently high levels of evidence and consensus may be included in a clinical pharmacogenomic test; however, result interpretation and phenotype prediction can be challenging for some genes and medications. This document provides a resource for laboratories to develop and implement clinical pharmacogenomic testing by summarizing publicly available resources and detailing best practices for pharmacogenomic nomenclature, testing, result interpretation, and reporting.

Association of Drug-Metabolizing Enzyme and Transporter Gene Polymorphisms and Lipid-Lowering Response to Statins in Thai Patients with Dyslipidemia

Purpose

Statins are increasingly widely used in the primary and secondary prevention of cardiovascular disease. However, there is an inter-individual variation in statin response among patients. The study aims to determine the association between genetic variations in drug-metabolizing enzyme and transporter (DMET) genes and lipid-lowering response to a statin in Thai patients with hyperlipidemia.

Patients and Methods

Seventy-nine patients who received statin at steady-state concentrations were recruited. Serum lipid profile was measured at baseline and repeated after 4-month on a statin regimen. The genotype profile of 1936 DMET markers was obtained using Affymetrix DMET Plus genotyping microarrays.

Results

In this DMET microarray platform, five variants; SLCO1B3 (rs4149117, rs7311358, and rs2053098), QPRT (rs13331798), and SLC10A2 (rs188096) showed a suggestive association with LDL-cholesterol-lowering response. HDL-cholesterol-lowering responses were found to be related to CYP7A1 gene variant (rs12542233). Seven variants, SLCO1B3 (rs4149117, rs7311358, and rs2053098); SULT1E1 (rs3736599 and rs3822172); and ABCB11 (rs4148768 and rs3770603), were associated with the total cholesterol-lowering response. One variant of the ABCB4 gene (rs2109505) was significantly associated with triglyceride-lowering response.

Conclusion

This pharmacogenomic study identifies new genetic variants of DMET genes that are associated with the lipid-lowering response to statins. Genetic polymorphisms in DMET genes may impact the pharmacokinetics and lipid-lowering response to statin. The validation studies confirmations are needed in future pharmacogenomic studies.

Tools in Pharmacogenomics Biomarker Identification for Cancer Patients

The understanding of the biological differences which underlie the inter-individual variability in drug response improved the efficacy of cancer therapy in the era of precision medicine. In fact molecularly targeted drugs and immunotherapy represent a revolution in cancer treatment. The identification of genetic predictive and/or prognostic biomarkers linked to drug pharmacokinetics (PK) and pharmacodynamics (PD) is allowed by the development of high-throughput omics tools for detecting and understanding biological differences among individuals, in order to improve drug efficacy and minimize risk of toxicity. Personalized medicine in cancer treatment reduces costs of the healthcare system. Unfortunately, pharmacogenomics biomarkers discovery is influenced by complexity, need of high-quality evidence, and a validation process for regulatory purposes. This chapter is focused on the critic analysis of presently available pharmacogenomics tools for discovering or testing genetic polymorphic variants in drug metabolizing enzyme to be introduced in clinical practice for the prospective stratification of cancer patients.

Using MMRFBiolinks R-Package for Discovering Prognostic Markers in Multiple Myeloma

Multiple myeloma (MM) is the second most frequent hematological malignancy in the world although the related pathogenesis remains unclear. Gene profiling studies, commonly carried out through next-generation sequencing (NGS) and Microarrays technologies, represent powerful tools for discovering prognostic markers in MM. NGS technologies have made great leaps forward both economically and technically gaining in popularity. As NGS techniques becomes simpler and cheaper, researchers choose NGS over microarrays for more of their genomic applications. However, Microarrays still provide significant benefits with respect to NGS. For instance, RNA-Seq requires more complex bioinformatic analysis with respect to Microarray as well as it lacks of standardized protocols for analysis. Therefore, a synergy between the two technologies may be well expected in the future. In order to take up this challenge, a valid tool for integrative analysis of MM data retrieved through NGS techniques is MMRFBiolinks, a new R package for integrating and analyzing datasets from the Multiple Myeloma Research Foundation (MMRF) CoMMpass (Clinical Outcomes in MM to Personal Assessment of Genetic Profile) study, available at MMRF Researcher Gateway (MMRF-RG), and at the National Cancer Institute Genomic Data Commons (NCI-GDC) Data Portal. Instead of developing a completely new package from scratch, we decided to leverage TC-GABiolinks, an R/Bioconductor package, because it provides some useful methods to access and analyze MMRF-CoMMpass data. An integrative analysis workflow based on the usage of MMRFBiolinks is illustrated.In particular, it leads towards a comparative analysis of RNA-Seq data stored at GDC Data Portal that allows to carry out a Kaplan Meier (KM ) Survival Analysis and an enrichment analysis for a Differential Gene Expression (DGE) gene set.Furthermore, it deals with MMRF-RG data for analyzing the correlation between canonical variants and treatment outcome as well as treatment class. In order to show the potential of the workflow, we present two case studies. The former deals with data of MM Bone Marrow sample types available at GDC Data Portal. The latter deals with MMRF-RG data for analyzing the correlation between canonical variants in a gene set obtained from the case study 1 and the treatment outcome as well as the treatment class.

Clustering Methods for Microarray Data Sets

Microarrays are experimental methods that can provide information about gene expression and SNP data that hold great potential for new understanding, driving advances in functional genomics and clinical and molecular biology. Cluster analysis is used to analyze data that are not a priori to contain any specific subgroup. The goal is to use the data itself to recognize meaningful and informative subgroups. Also, cluster analysis helps data reduction purposes, exposes hidden patterns, and generates hypotheses regarding the relationship between genes and phenotypes. This chapter outlines a collection of cluster methods suitable for the analysis of microarray data sets.

Comprehensive analysis of important pharmacogenes in Koreans using the DMET™ platform

Genetic polymorphisms of enzymes and transporters associated with the absorption, distribution, metabolism, and elimination (ADME) of drugs are one of the major factors that contribute to interindividual variations in drug response. In the present study, we aimed to elucidate the pharmacogenetic profiles of the Korean population using the Affymetrix Drug Metabolizing Enzyme and Transporters (DMET™) platform. A total of 1,012 whole blood samples collected from Korean subjects were genotyped using the DMET™ plus microarray. In total, 1,785 single nucleotide polymorphism (SNP) markers for 231 ADME genes were identified. The genotype and phenotype of 13 clinically important ADME genes implemented in the Clinical Pharmacogenetics Implementation Consortium guidelines were compared among different ethnic groups. Overall, the genotype frequencies of the Korean population were similar to those of the East Asian population. Several genes, notably CYP2C19 and VKORC1, showed marked differences in Koreans compared to Europeans (EURs) or Africans (AFRs). The percentage of CYP2C19 poor metabolizers was 15% in Koreans and less than 3% in EURs or AFRs. The frequencies of causative SNPs of the VKORC1 gene for the low warfarin dose phenotype were 90%, 60%, and 10% in Koreans, EURs and AFRs, respectively. Our findings can be utilized for optimal pharmacotherapy in Korean patients.

Pharmacogenetic Predictors of Cannabidiol Response and Tolerability in Treatment-Resistant Epilepsy

In patients with treatment‐resistant epilepsy (TRE), cannabidiol (CBD) produces variable improvement in seizure control. Patients in the University of Alabama at Birmingham CBD Expanded Access Program (EAP) were enrolled in the genomic study and genotyped using the Affymetrix Drug Metabolizing Enzymes and Transporters plus array. Associations between variants and CBD response (≥50% seizure reduction) and tolerability (diarrhea, sedation, and abnormal liver function) was evaluated under dominant and recessive models. Expression quantitative trait loci (eQTL) influencing potential CBD targets was evaluated in the UK Brain Expression Consortium data set (Braineac), and genetic co‐expression examined. Of 169 EAP patients, 112 (54.5% pediatric and 50.0% female) were included in the genetic analyses. Patients with AOX1 rs6729738 CC (aldehyde oxidase; odds ratio (OR) 6.69, 95% confidence interval (CI) 2.19–20.41, P = 0.001) or ABP1 rs12539 (diamine oxidase; OR 3.96, 95% CI 1.62–9.73, P = 0.002) were more likely to respond. Conversely, patients with SLC15A1 rs1339067 TT had lower odds of response (OR 0.06, 95% CI 0.01–0.56, P = 0.001). ABCC5 rs3749442 was associated with lower likelihood of response and abnormal liver function tests, and higher likelihood of sedation. The eQTL revealed that rs1339067 decreased GPR18 expression (endocannabinoid receptor) in white matter (P = 5.6 × 10⁻³), and rs3749442 decreased hippocampal HTR3E expression (serotonin 5‐HT_3E; P = 8.5 × 10⁻⁵). Furthermore, 75% of genes associated with lower likelihood of response were co‐expressed. Pharmacogenetic variation is associated with CBD response and influences expression of CBD targets in TRE. Implicated pathways, including cholesterol metabolism and glutathione conjugation, demonstrate potential interactions between CBD and common medications (e.g., statins and acetaminophen) that may require closer monitoring. These results highlight the role of pharmacogenes in fundamental biologic processes and potential genetic underpinnings of treatment‐resistance.

Applying Next-Generation Sequencing Platforms for Pharmacogenomic Testing in Clinical Practice

Pharmacogenomics (PGx) studies the use of genetic data to optimize drug therapy. Numerous clinical centers have commenced implementing pharmacogenetic tests in clinical routines. Next-generation sequencing (NGS) technologies are emerging as a more comprehensive and time- and cost-effective approach in PGx. This review presents the main considerations for applying NGS in guiding drug treatment in clinical practice. It discusses both the advantages and the challenges of implementing NGS-based tests in PGx. Moreover, the limitations of each NGS platform are revealed, and the solutions for setting up and management of these technologies in clinical practice are addressed.

Planning and Conducting a Pharmacogenetics Association Study

Pharmacogenetics (PGx) association studies are used to discover, replicate, and validate the association between an inherited genotype and a treatment outcome. The objective of this tutorial is to provide trainees and novice PGx researchers with an overview of the major decisions that need to be made when designing and conducting a PGx association study. The first critical decision is to determine whether the objective of the study is discovery, replication, or validation. Next, the researcher must identify a patient cohort that has all of the data necessary to conduct the intended analysis. Then, the investigator must select and define the treatment outcome, or phenotype, that will be analyzed. Next, the investigator must determine what genotyping approach and genetic data will be included in the analysis. Finally, the association between the genotype and phenotype is tested using some statistical analysis methodology. This tutorial is divided into five sections; each section describes commonly used approaches and provides suggestions and resources for designing and conducting a PGx association study. Successful PGx association studies are necessary to discover and validate associations between inherited genetic variation and treatment outcomes, which enable clinical translation to improve efficacy and reduce toxicity of treatment.

PharmaKU: A Web-Based Tool Aimed at Improving Outreach and Clinical Utility of Pharmacogenomics

With the tremendous advancements in genome sequencing technology in the field of pharmacogenomics, data have to be made accessible to be more efficiently utilized by broader clinical disciplines. Physicians who require the drug–genome interactome information, have been challenged by the complicated pharmacogenomic star-based classification system. We present here an end-to-end web-based pharmacogenomics tool, PharmaKU, which has a comprehensive easy-to-use interface. PharmaKU can help to overcome several hurdles posed by previous pharmacogenomics tools, including input in hg38 format only, while hg19/GRCh37 is now the most popular reference genome assembly among clinicians and geneticists, as well as the lack of clinical recommendations and other pertinent dosage-related information. This tool extracts genetic variants from nine well-annotated pharmacogenes (for which diplotype to phenotype information is available) from whole genome variant files and uses Stargazer software to assign diplotypes and apply prescribing recommendations from pharmacogenomic resources. The tool is wrapped with a user-friendly web interface, which allows for choosing hg19 or hg38 as the reference genome version and reports results as a comprehensive PDF document. PharmaKU is anticipated to enable bench to bedside implementation of pharmacogenomics knowledge by bringing precision medicine closer to a clinical reality.

Pharmacogenomics with red cells: a model to study protein variants of drug transporter genes

The PharmacoScan pharmacogenomics platform screens for variation in genes that affect drug absorption, distribution, metabolism, elimination, immune adverse reactions and targets. Among the 1,191 genes tested on the platform, 12 genes are expressed in the red cell membrane: ABCC1, ABCC4, ABCC5, ABCG2, CFTR, SLC16A1, SLC19A1, SLC29A1, ATP7A, CYP4F3, EPHX1 and FLOT1. These genes represent 5 ATP-binding cassette proteins, 3 solute carrier proteins, 1 ATP transport protein and 3 genes associated with drug metabolism and adverse drug reactions. Only ABCG2 and SLC29A1 encode blood group systems, JR and AUG, respectively. We propose red cells as an ex vivo model system to study the effect of heritable variants in genes encoding the transport proteins on the pharmacokinetics of drugs. Altered pharmacodynamics in red cells could also cause adverse reactions, such as haemolysis, hitherto unexplained by other mechanisms.

Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics

Metabolomics has been widely used for investigating the biological functions of disease expression and has the potential to discover biomarkers in circulating biofluids or tissue extracts that reflect in phenotypic changes. Metabolic profiling has advantages because of the use of unbiased techniques, including multivariate analysis, and has been applied in pharmacological studies to predict therapeutic and adverse reactions of drugs, which is called pharmacometabolomics (PMx). Nuclear magnetic resonance (NMR)- and mass spectrometry (MS)-based metabolomics has contributed to the discovery of recent disease biomarkers; however, the optimal strategy for the study purpose must be selected from many established protocols, methodologies and analytical platforms. Additionally, information on molecular localization in tissue is essential for further functional analyses related to therapeutic and adverse effects of drugs in the process of drug development. MS imaging (MSI) is a promising technology that can visualize molecules on tissue surfaces without labeling and thus provide localized information. This review summarizes recent uses of MS-based global and wide-targeted metabolomics technologies and the advantages of the MSI approach for PMx and highlights the PMx technique for the biomarker discovery of adverse drug effects.

Technologies for Pharmacogenomics: A Review

The continuous development of new genotyping technologies requires awareness of their potential advantages and limitations concerning utility for pharmacogenomics (PGx). In this review, we provide an overview of technologies that can be applied in PGx research and clinical practice. Most commonly used are single nucleotide variant (SNV) panels which contain a pre-selected panel of genetic variants. SNV panels offer a short turnaround time and straightforward interpretation, making them suitable for clinical practice. However, they are limited in their ability to assess rare and structural variants. Next-generation sequencing (NGS) and long-read sequencing are promising technologies for the field of PGx research. Both NGS and long-read sequencing often provide more data and more options with regard to deciphering structural and rare variants compared to SNV panels-in particular, in regard to the number of variants that can be identified, as well as the option for haplotype phasing. Nonetheless, while useful for research, not all sequencing data can be applied to clinical practice yet. Ultimately, selecting the right technology is not a matter of fact but a matter of choosing the right technique for the right problem.

Clinical outcome and toxicity from taxanes in breast cancer patients with BRCA1 and BRCA2 pathogenic germline mutations

Germline variations in genes coding for proteins involved in the oxidative stress and DNA repair greatly influence drug response and toxicity. Because BRCA1 and BRCA2 proteins play a role in DNA damage repair, we postulated that taxane-related toxicity is potentially higher and clinical outcome in different in patients with BRCA pathogenic variants (PV). Seven hundred nineteen women who underwent BRCA genetic testing and were treated with taxane-containing chemotherapy for early-stage breast cancer between 1997 and 2018 were included in the study. Patients with BRCA variants of uncertain significance were excluded. The Kaplan-Meier product-limit method was used to estimate recurrence-free survival (RFS) and overall survival (OS) rates. Logistic regression models were used to assess the association between chemotherapy toxicity and factors of interest. Cox regression models were used to assess the association between RFS and OS and factors of interest. Ninety-four (13%) and 54 (7%) patients had BRCA1 and BRCA2-PVs, respectively. While anemia (P = .0025) and leukopenia (P = .001) were more frequently seen in BRCA noncarriers, there was no difference in regards to peripheral neuropathy or other toxicities between the groups. Increasing doses of taxane were associated with increased risk of neutropenia, stomatitis, nausea, vomiting, acne/rash, and peripheral neuropathy across all groups. In a multivariate logistic regression model, BRCA2 status remained as an independent significant predictor for decreased hematologic toxicity (HR: 0.36; 95% CI: 0.20-0.67; P = .001) and increased gastrointestinal toxicity (HR: 1.93; 95% CI: 1.02-3.67; P = .04). Being overweight, obese and African-American race were significant predictors for peripheral neuropathy (P = .04; P = .03; P = .06, respectively). Total taxane dose received did not have any impact on survival outcomes. Our study demonstrates that taxane-containing chemotherapy regimens do not increase risk of peripheral neuropathy or hematologic toxicity in patients with BRCA PVs. The mechanisms for this finding need to be further investigated as it may provide an opportunity to combine taxanes with other agents, such as platinum salts or PARP inhibitors, with less anticipated toxicity.

Population Pharmacokinetic and Pharmacogenetic Analysis of Mitotane in Patients with Adrenocortical Carcinoma: Towards Individualized Dosing

Background

Mitotane is the only approved treatment for patients with adrenocortical carcinoma (ACC). A better explanation for the variability in the pharmacokinetics (PK) of mitotane, and the optimization and individualization of mitotane treatment, is desirable for patients.

Objectives

This study aims to develop a population PK (PopPK) model to characterize and predict the PK profiles of mitotane in patients with ACC, as well as to explore the effect of genetic variation on mitotane clearance. Ultimately, we aimed to facilitate mitotane dose optimization and individualization for patients with ACC.

Methods

Mitotane concentration and dosing data were collected retrospectively from the medical records of patients with ACC taking mitotane orally and participating in the Dutch Adrenal Network. PopPK modelling analysis was performed using NONMEM (version 7.4.1). Genotypes of drug enzymes and transporters, patient demographic information, and clinical characteristics were investigated as covariates. Subsequently, simulations were performed for optimizing treatment regimens.

Results

A two-compartment model with first-order absorption and elimination best described the PK data of mitotane collected from 48 patients. Lean body weight (LBW) and genotypes of CYP2C19*2 (rs4244285), SLCO1B3 699A>G (rs7311358) and SLCO1B1 571T>C (rs4149057) were found to significantly affect mitotane clearance (CL/F), which decreased the coefficient of variation (CV%) of the random inter-individual variability of CL/F from 67.0 to 43.0%. Fat amount (i.e. body weight − LBW) was found to significantly affect the central distribution volume. Simulation results indicated that determining the starting dose using the developed model is beneficial in terms of shortening the period to reach the therapeutic target and limit the risk of toxicity. A regimen that can effectively maintain mitotane concentration within 14–20 mg/L was established.

Conclusions

A two-compartment PopPK model well-characterized mitotane PK profiles in patients with ACC. The CYP2C19 enzyme and SLCO1B1 and SLCO1B3 transporters may play roles in mitotane disposition. The developed model is beneficial in terms of optimizing mitotane treatment schedules and individualizing the initial dose for patients with ACC. Further validation of these findings is still required.

Electronic supplementary material

The online version of this article (10.1007/s40262-020-00913-y) contains supplementary material, which is available to authorized users.

Exome-Wide Analysis of the DiscovEHR Cohort Reveals Novel Candidate Pharmacogenomic Variants for Clinical Pharmacogenomics

Recent advances in next-generation sequencing technology have led to the production of an unprecedented volume of genomic data, thus further advancing our understanding of the role of genetic variation in clinical pharmacogenomics. In the present study, we used whole exome sequencing data from 50,726 participants, as derived from the DiscovEHR cohort, to identify pharmacogenomic variants of potential clinical relevance, according to their occurrence within the PharmGKB database. We further assessed the distribution of the identified rare and common pharmacogenomics variants amongst different GnomAD subpopulations. Overall, our findings show that the use of publicly available sequence data, such as the DiscovEHR dataset and GnomAD, provides an opportunity for a deeper understanding of genetic variation in pharmacogenes with direct implications in clinical pharmacogenomics.

Genetic 3'UTR variations and clinical factors significantly contribute to survival prediction and clinical response in breast cancer patients

The study describes a relationship between the 3′UTR variants, clinicopathological parameters and response to chemotherapy. We analyzed 33 germline polymorphisms in 3′UTRs of ADME genes in 305 breast cancer women treated with FAC regime. Clinical endpoints of this study were: overall survival (OS), progression-free survival (PFS), recurrence-free survival (RFS) and overall response defined as treatment failure-free survival (TFFS). The shortened OS was connected with the presence of NR1/2 rs3732359 AA, SLC22A16 rs7756222 CC, as well as SLC22A16 rs9487402 allele G and clinical factors belonging to TNM classification: tumor size >1 cm, nodal involvement and presence of metastases. PFS was related to two polymorphisms PGR rs1824125 GG, PGR rs12224560 CC and SLC22A16 rs7756222 CC as well as preexisting metastases. The RFS was shortened due to the DPYD rs291593 CC, AKR1C3 rs3209896 AG and negative expression of PGR. The presence of ALDH5A1 rs1054899 allele A, lack of pre-chemotherapy surgery and negative status of PGR correlated with worse treatment response. The germline variants commonly present in the population are important factors determining the response to treatment. We observed the effect of the accumulation of genetic and clinical factors on poor survival prognosis and overall treatment response.

DMETTM Genotyping: Tools for Biomarkers Discovery in the Era of Precision Medicine

The knowledge of genetic variants in genes involved in drug metabolism may be translated into reduction of adverse drug reactions, increase of efficacy, healthcare outcomes improvement and economic benefits. Many high-throughput tools are available for the genotyping of Single Nucleotide Polymorphisms (SNPs) known to be related to drugs and xenobiotics metabolism. DMET^TM platform represents an example of SNPs panel to discover biomarkers correlated to efficacy or toxicity in common and rare diseases. The difficulty in analyzing the mole of information generated by DMET^TM platform led to the development and implementation of algorithms and tools for statistical and data mining analysis. These softwares allow efficient handling of the omics data to validate the explorative SNPs identified by DMET assay and to correlate them with drug efficacy, toxicity and/or cancer susceptibility. In this review we present a suite of bioinformatic frameworks for the preprocessing and analysis of DMET-SNPs data. In particular, we introduce a workflow that uses the GenoMetric Query Language, a high-level query language specifically designed for genomics, able to query public datasets (such as ENCODE, TCGA, GENCODE annotation dataset, etc.) as well as to combine them with private datasets (e.g., output from Affymetrix® DMET^TM Platform).

A high-resolution HLA imputation system for the Taiwanese population: a study of the Taiwan Biobank

An imputation algorithm for human leukocyte antigen (HLA) is helpful for exploring novel disease associations. However, population-specific HLA imputation references are essential for achieving high imputation accuracy. In this study, a subset of 1012 individuals from the Taiwan Biobank (TWB) who underwent both whole-genome SNP array and NGS-based HLA typing were used to establish Taiwanese HLA imputation references. The HIBAG package was used to generate the imputation references for eight HLA loci at a two- and three-field resolution. Internal validation was carried out to evaluate the call threshold and accuracy for each HLA gene. HLA class II genes found to be associated with rheumatoid arthritis (RA) were validated in this study by the imputed HLA alleles. Our Taiwanese population-specific references achieved average HLA imputation accuracies of 98.11% for two-field and 98.08% for three-field resolution. The frequency distribution of imputed HLA alleles among 23,972 TWB subjects were comparable with PCR-based HLA alleles in general Taiwanese reported in the allele frequency net database. We replicated four common HLA alleles (HLA-DRB1*03:01, DRB1*04:05, DQA1*03:03, and DQB1*04:01) significantly associated with RA. The population-specific references provide an informative tool to investigate the associations of HLA variants and human diseases in large-scale population-based studies.

Omics for the future in asthma

Asthma is a common, complex, multifaceted disease. It comprises multiple phenotypes, which might benefit from treatment with different types of innovative targeted therapies. Refining these phenotypes and understanding their underlying biological structure would help to apply precision medicine approaches. Using different omics methods, such as (epi)genomics, transcriptomics, proteomics, metabolomics, microbiomics, and exposomics, allowed to view and investigate asthma from diverse angles. Technological advancement led to a large increase in the application of omics studies in the asthma field. Although the use of omics technologies has reduced the gap between bench to bedside, several design and methodological challenges still need to be tackled before omics can be applied in asthma patient care. Collaborating under a centralized harmonized work frame (such as in consortia, under consistent methodologies) could help worldwide research teams to tackle these challenges. In this review, we discuss the transition of single biomarker research to multi-omics studies. In addition, we deliberate challenges such as the lack of standardization of sampling and analytical methodologies and validation of findings, which comes in between omics and personalized patient care. The future of omics in asthma is encouraging but not completely clear with some unanswered questions, which have not been adequately addressed before. Therefore, we highlight these questions and emphasize on the importance of fulfilling them.

Translating pharmacogenomics into clinical decisions: do not let the perfect be the enemy of the good

The field of pharmacogenomics (PGx) is gradually shifting from the reactive testing of single genes toward the proactive testing of multiple genes to improve treatment outcomes, reduce adverse events, and decrease the burden of unnecessary costs for healthcare systems. Despite the progress in the field of pharmacogenomics, its implementation into routine care has been slow due to several barriers. However, in recent years, the number of studies on the implementation of PGx has increased, all providing a wealth of knowledge on different solutions for overcoming the obstacles that have been emphasized over the past years. This review focuses on some of the challenges faced by these initiatives, the solutions and different approaches for testing that they suggest, and the evidence that they provide regarding the benefits of preemptive PGx testing.

A Network of SLC and ABC Transporter and DME Genes Involved in Remote Sensing and Signaling in the Gut-Liver-Kidney Axis

Genes central to drug absorption, distribution, metabolism and elimination (ADME) also regulate numerous endogenous molecules. The Remote Sensing and Signaling Hypothesis argues that an ADME gene-centered network-including SLC and ABC "drug" transporters, "drug" metabolizing enzymes (DMEs), and regulatory genes-is essential for inter-organ communication via metabolites, signaling molecules, antioxidants, gut microbiome products, uremic solutes, and uremic toxins. By cross-tissue co-expression network analysis, the gut, liver, and kidney (GLK) formed highly connected tissue-specific clusters of SLC transporters, ABC transporters, and DMEs. SLC22, SLC25 and SLC35 families were network hubs, having more inter-organ and intra-organ connections than other families. Analysis of the GLK network revealed key physiological pathways (e.g., involving bile acids and uric acid). A search for additional genes interacting with the network identified HNF4α, HNF1α, and PXR. Knockout gene expression data confirmed ~60-70% of predictions of ADME gene regulation by these transcription factors. Using the GLK network and known ADME genes, we built a tentative gut-liver-kidney "remote sensing and signaling network" consisting of SLC and ABC transporters, as well as DMEs and regulatory proteins. Together with protein-protein interactions to prioritize likely functional connections, this network suggests how multi-specificity combines with oligo-specificity and mono-specificity to regulate homeostasis of numerous endogenous small molecules.

Severe Hepatotoxicity of Mithramycin Therapy Caused by Altered Expression of Hepatocellular Bile Transporters

Mithramycin demonstrates preclinical anticancer activity, but its therapeutic dose is limited by the development of hepatotoxicity that remains poorly characterized. A pharmacogenomics characterization of mithramycin-induced transaminitis revealed that hepatotoxicity is associated with germline variants in genes involved in bile disposition: ABCB4 (multidrug resistance 3) rs2302387 and ABCB11 [bile salt export pump (BSEP)] rs4668115 reduce transporter expression (P < 0.05) and were associated with ≥grade 3 transaminitis developing 24 hours after the third infusion of mithramycin (25 mcg/kg, 6 hours/infusion, every day ×7, every 28 days; P < 0.0040). A similar relationship was observed in a pediatric cohort. We therefore undertook to characterize the mechanism of mithramycin-induced acute transaminitis. As mithramycin affects cellular response to bile acid treatment by altering the expression of multiple bile transporters (e.g., ABCB4, ABCB11, sodium/taurocholate cotransporting polypeptide, organic solute transporter α/β) in several cell lines [Huh7, HepaRG, HepaRG BSEP (-/-)] and primary human hepatocytes, we hypothesized that mithramycin inhibited bile-mediated activation of the farnesoid X receptor (FXR). FXR was downregulated in all hepatocyte cell lines and primary human hepatocytes (P < 0.0001), and mithramycin inhibited chenodeoxycholic acid- and GW4046-induced FXR-galactose-induced gene 4 luciferase reporter activity (P < 0.001). Mithramycin promoted glycochenodeoxycholic acid-induced cytotoxicity in ABCB11 (-/-) cells and increased the overall intracellular concentration of bile acids in primary human hepatocytes grown in sandwich culture (P < 0.01). Mithramycin is a FXR expression and FXR transactivation inhibitor that inhibits bile flow and potentiates bile-induced cellular toxicity, particularly in cells with low ABCB11 function. These results suggest that mithramycin causes hepatotoxicity through derangement of bile acid disposition; results also suggest that pharmacogenomic markers may be useful to identify patients who may tolerate higher mithramycin doses. SIGNIFICANCE STATEMENT: The present study characterizes a novel mechanism of drug-induced hepatotoxicity in which mithramycin not only alters farnesoid X receptor (FXR) and small heterodimer partner gene expression but also inhibits bile acid binding to FXR, resulting in deregulation of cellular bile homeostasis. Two novel single-nucleotide polymorphisms in bile flow transporters are associated with mithramycin-induced liver function test elevations, and the present results are the rationale for a genotype-directed clinical trial using mithramycin in patients with thoracic malignancies.

Polymorphic Variants in NR1I3 and UGT2B7 Predict Taxane Neurotoxicity and Have Prognostic Relevance in Patients With Breast Cancer: A Case-Control Study

Taxane-related peripheral neuropathy (TrPN) is a dose-limiting toxicity with important interindividual variability. Genetic polymorphisms in absorption, distribution, metabolism, and excretion (ADME) genes may account for variability in drug efficacy and/or toxicity. By the use of Affymetrix drug-metabolizing enzyme and transporter microarray platform, in a retrospective case-control study, the correlation between ADME polymorphic variants and grades ≥ 2-3-TrPN was investigated. In a breast cancer (BC) training set, five single-nucleotide polymorphisms in NR1I3 and UDP-glucuronosyltransferase (UGT)2B7 genes were correlated to grades ≥ 2-3-TrPN protection. By receiver operating characteristic curves, the grades ≥ 2-3-TrPN-related candidate biomarkers in an independent series of 54 patients with BC (17 cases and 37 controls) were validated. NR1I3 was correlated to paclitaxel-TrPN and UGT2B7 to docetaxel-TrPN. Moreover, a genetic signature of prognostic relevance for BC outcome was found. Our findings might have potential relevance for personalized management of patients with BC for prevention of treatment failure in ultrametabolizer genetic variants.

Contemporary pharmacogenetic assays in view of the PharmGKB database

AIM

Six modern PGx assays were compared with the Pharmacogenomics Knowledge Base (PharmGKB) to determine the proportion of the currently known PGx genotypes that are assessed by these assays.

MATERIALS & METHODS

Investigated assays were 'Ion AmpliSeq Pharmacogenomics', 'iPLEX PGx Pro', 'DMET Plus,' 'PharmcoScan,' 'Living DNA' and '23andMe.'

RESULTS

PharmGKB contains 3474 clinical annotations of which 75, 70 and 45% can be determined by PharmacoScan, Living DNA and 23andMe, respectively. The other assays are designed to test a specific subset of PGx variants.

CONCLUSION

Assaying all known PGx variants would only comprise a minor fraction of the current assays' capacity. Unfortunately, this is not achieved. Moreover, not necessarily the variants with the highest effects or the highest evidence are selected.

Pharmacogenomic Profiling of ADME Gene Variants: Current Challenges and Validation Perspectives

In the past decades, many efforts have been made to individualize medical treatments, taking into account molecular profiles and the individual genetic background. The development of molecularly targeted drugs and immunotherapy have revolutionized medical treatments but the inter-patient variability in the anti-tumor drug pharmacokinetics (PK) and pharmacodynamics can be explained, at least in part, by genetic variations in genes encoding drug metabolizing enzymes and transporters (ADME) or in genes encoding drug receptors. Here, we focus on high-throughput technologies applied for PK screening for the identification of predictive biomarkers of efficacy or toxicity in cancer treatment, whose application in clinical practice could promote personalized treatments tailored on individual's genetic make-up. Pharmacogenomic tools have been implemented and the clinical utility of pharmacogenetic screening could increase safety in patients for the identification of drug metabolism-related biomarkers for a personalized medicine. Although pharmacogenomic studies were performed in adult cohorts, pharmacogenetic pediatric research has yielded promising results. Additionally, we discuss the current challenges and theoretical bases for the implementation of pharmacogenetic tests for translation in the clinical practice taking into account that pharmacogenomics platforms are discovery oriented and must open the way for the setting of robust tests suitable for daily practice.

The Cytoscan HD Array in the Diagnosis of Neurodevelopmental Disorders

Submicroscopic chromosomal copy number variations (CNVs), such as deletions and duplications, account for about 15–20% of patients affected with developmental delay, intellectual disability, multiple congenital anomalies, and autism spectrum disorder. Most of CNVs are de novo or inherited rearrangements with clinical relevance, but there are also rare inherited imbalances with unknown significance that make difficult the clinical management and genetic counselling. Chromosomal microarrays analysis (CMA) are recognized as the first-line test for CNV detection and are now routinely used in the clinical diagnostic laboratory. The recent use of CMA platforms that combine classic copy number analysis with single-nucleotide polymorphism (SNP) genotyping has increased the diagnostic yields. Here we discuss the application of the Cytoscan high-density (HD) SNP-array for the detection of CNVs. We provide an overview of molecular analyses involved in identifying pathogenic CNVs and highlight important guidelines to establish pathogenicity of CNV.

UGT1A1 polymorphisms associated with prolactin response in risperidone-treated children and adolescents with autism spectrum disorder

The aim of this study was to investigate the association of drug-metabolizing enzyme and transporter (DMET) polymorphisms with the risperidone-induced prolactin response using an overlapping gene model between serum prolactin level and hyperprolactinemia in autism spectrum disorder (ASD) patients. Eighty-four ASD patients who were receiving risperidone for at least 1 month were recruited and then assigned to either the normal prolactin group or the hyperprolactinemia group based on their serum prolactin level. The genotype profile of 1936 (1931 single nucleotide polymorphisms (SNPs) and 5 copy number variation (CNVs) drug metabolism markers was obtained using the Affymetrix DMET Plus GeneChip microarray platform. Genotypes of SNPs used to test the accuracy of DMET genotype profiling were determined using TaqMan SNP Genotyping Assay kits. Eighty-four patients were selected for the allelic association study after microarray analyses (51 in the normal prolactin group, and 33 in the hyperprolactinemia group). An overlapping allelic association analysis of both analyses discovered five DMET SNPs with a suggestive association (P < 0.05) with risperidone-induced prolactin response. Three UGT1A1 SNPs (UGT1A1*80c.-364C > T, UGT1A1*93 c.-3156G > A, and UGT1A1 c.-2950A > G, showed a suggestive association with the risperidone-induced prolactin response and found to be in complete linkage disequilibrium (D' value of 1). In this DMET microarray platform, we found three UGT1A1 variants with suggestive evidences of association with the risperidone-induced prolactin response both measured by hyperprolactinemia and by prolactin level. However, due to the lack of validation studies confirmation and further exploration are needed in future pharmacogenomic studies.

A Parallel Software Pipeline for DMET Microarray Genotyping Data Analysis

Personalized medicine is an aspect of the P4 medicine (predictive, preventive, personalized and participatory) based precisely on the customization of all medical characters of each subject. In personalized medicine, the development of medical treatments and drugs is tailored to the individual characteristics and needs of each subject, according to the study of diseases at different scales from genotype to phenotype scale. To make concrete the goal of personalized medicine, it is necessary to employ high-throughput methodologies such as Next Generation Sequencing (NGS), Genome-Wide Association Studies (GWAS), Mass Spectrometry or Microarrays, that are able to investigate a single disease from a broader perspective. A side effect of high-throughput methodologies is the massive amount of data produced for each single experiment, that poses several challenges (e.g., high execution time and required memory) to bioinformatic software. Thus a main requirement of modern bioinformatic softwares, is the use of good software engineering methods and efficient programming techniques, able to face those challenges, that include the use of parallel programming and efficient and compact data structures. This paper presents the design and the experimentation of a comprehensive software pipeline, named microPipe, for the preprocessing, annotation and analysis of microarray-based Single Nucleotide Polymorphism (SNP) genotyping data. A use case in pharmacogenomics is presented. The main advantages of using microPipe are: the reduction of errors that may happen when trying to make data compatible among different tools; the possibility to analyze in parallel huge datasets; the easy annotation and integration of data. microPipe is available under Creative Commons license, and is freely downloadable for academic and not-for-profit institutions.

Pharmacogenetic landscape of Metabolic Syndrome components drug response in Tunisia and comparison with worldwide populations

Genetic variation is an important determinant affecting either drug response or susceptibility to adverse drug reactions. Several studies have highlighted the importance of ethnicity in influencing drug response variability that should be considered during drug development. Our objective is to characterize the genetic variability of some pharmacogenes involved in the response to drugs used for the treatment of Metabolic Syndrome (MetS) in Tunisia and to compare our results to the worldwide populations. A set of 135 Tunisians was genotyped using the Affymetrix Chip 6.0 genotyping array. Variants located in 24 Very Important Pharmacogenes (VIP) involved in MetS drug response were extracted from the genotyping data. Analysis of variant distribution in Tunisian population compared to 20 worldwide populations publicly available was performed using R software packages. Common variants between Tunisians and the 20 investigated populations were extracted from genotyping data. Multidimensional screening showed that Tunisian population is clustered with North African and European populations. The greatest divergence was observed with the African and Asian population. In addition, we performed Inter-ethnic comparison based on the genotype frequencies of five VIP biomarkers. The genotype frequencies of the biomarkers rs3846662, rs1045642, rs7294 and rs12255372 located respectively in HMGCR, ABCB1, VKORC1 and TCF7L2 are similar between Tunisian, Tuscan (TSI) and European (CEU). The genotype frequency of the variant rs776746 located in CYP3A5 gene is similar between Tunisian and African populations and different from CEU and TSI. The present study shows that the genetic make up of the Tunisian population is relatively complex in regard to pharmacogenes and reflects previous historical events. It is important to consider this ethnic difference in drug prescription in order to optimize drug response to avoid serious adverse drug reactions. Taking into account similarities with other neighboring populations, our study has an impact not only on the Tunisian population but also on North African population which are underrepresented in pharmacogenomic studies.

Single nucleotide polymorphisms in CIDEC gene are associated with metabolic syndrome components risks and antihypertensive drug efficacy

The association of single nucleotide polymorphisms rs1053239 and rs2479 of cell death-inducing DFFA-like effector c with the risk of metabolic syndrome and its components, and with the efficacy and cost-effectiveness of antihypertensive drugs was investigated. Totally 1064 subjects with metabolic syndrome and 1099 controls of Chinese Han nationality were recruited. Clinical assessment was conducted with medication records collected at baseline and during 5-year follow-up. Carriers of rs2479 A allele were at higher risk to develop elevated fasting glucose than non-carriers (P = 0.004). A allele at rs2479 were associated with a 5-year aggravation of blood triglyceride (P < 0.001) and diastolic blood pressure (P = 0.003), and C allele at rs1053239 with the exacerbation of systolic (P < 0.001) and diastolic blood pressure (P = 0.001). Moreover, efficacy and cost-effectiveness of angiotensin II-targeted drugs were higher in subjects with rs2479 A allele or rs1053239 C allele. These findings suggest that carriers of rs2479 A allele are predisposed to the development of increased fasting glucose, and the progressive elevation of blood triglyceride. Individuals with A allele at rs2479 or C allele at rs1053239 are more susceptible to a rapid progression of blood pressure, and benefit more from angiotensin II-targeted therapy.

Integrating pharmacogenetic testing into primary care

Introduction

Pharmacogenetic (PGx) testing has greatly expanded due to enhanced understanding of the role of genes in drug response and advances in DNA-based testing technology development. As many primary care visits result in a prescription, the use of PGx testing may be particularly beneficial in this setting. However, integration of PGx testing may be limited as no uniform approach to delivery of tests has been established and providers are ill-prepared to integrate PGx testing into routine care.

Areas covered

In this paper, the readiness of primary care practitioners are reviewed as well as strategies to address these barriers based on published research and ongoing activities on education and implementation of PGx testing.

Expert Commentary

Widespread integration of PGx testing will warrant continued education and point-of-care decisional support. Primary care providers may also benefit from consultation services or team-based care with laboratory medicine specialists, pharmacists, and genetic counselors.